https://github.com/Sanofi-Public/LipoBART
https://github.com/Sanofi-Public/LipoBART
Last synced: 4 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/Sanofi-Public/LipoBART
- Owner: Sanofi-Public
- License: other
- Created: 2024-02-21T17:06:07.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-02T13:47:00.000Z (7 months ago)
- Last Synced: 2025-01-11T04:35:58.200Z (4 months ago)
- Language: Jupyter Notebook
- Size: 5.32 MB
- Stars: 7
- Watchers: 0
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# Lipid Nanoparticle Design
Repository containing data, code and walkthrough for methods in the paper [*Representations of lipid nanoparticles using large language models for transfection efficiency prediction*](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btae342/7684951).
We aim to design LNPs for delivery of mRNA therapeutics. With this goal in mind we therefore define a successful LNP as one that has following attributes: biodegradeble, non-toxic, synthesizable, stable (pH/temperature) and (most importantly) transfection efficiency.
We design predictive models that estimate these qualities with the intention of using them in screening/selecting the best candidates for experimental testing and development.
We reproduce the results of [*Ding et al., 2023*](https://arxiv.org/abs/2308.01402) in this repository. We then also compare how are methods compare on this benchmark dataset.
## Environment Setup
Dependency management is done via [poetry](https://python-poetry.org/).
```
pip install poetry
pip install tensorflow # tensorflow is installed separately
poetry install
```
## Package Structure
We organize our code into the following structure:
- `data`: contains all data used in our experiments. This includes CSVs with lipids SMILES and properties, and corresponding fingerprint JSON files from different embedding methods. Those ending in `_alldata` correspond to SMILES from *Ding et al*.
- `src`: contains the source code for downloading data from *Ding et al.*, splitting data for multiclass classification, running the tournament script for classification, and some helpers.
- `notebooks`: contains notebooks for data exploration, as well as gcn training and embedding extraction, and downstream classification with different methods.
## Fine-Tuning MegaMolBART
We found that the best performing model for predicting LNP transfection efficiency relies on embedding LNPs with a large language model. Here we outline our method for finetuning NVIDIA's MegaMolBART model on the swisslipid dataset.
1. Download, install and setup the MegaMolBART pre-trained model. We recommend installing using the container.
- [repository](https://github.com/NVIDIA/MegaMolBART)
- [tutorial](https://docs.nvidia.com/bionemo-framework/0.4.0/notebooks/MMB_GenerativeAI_Inference_with_examples.html)
2. Download the [swisslipid dataset](https://www.swisslipids.org/)
3. Edit the model configuration file such that (i.e. `megamolbart_pretrain_base.yaml`):
- `restore_from_path` points to the path of the pre-trained MegaMolBART `.nemo` file
- adjust `trainer` and `batch_size` params optimal to your system settings (number and memory of GPUs)
- `dataset_path` is set to the location of the downloaded swisslipid dataset.
- *edit the swisslipid dataset such that it conforms to the datamodel of the MegaMolBART example dataset. Split the data into 99% training 0.5% test, 0.5% validation*
4. Run the pre-training script with this configuration ([instructions here](https://github.com/NVIDIA/MegaMolBART/blob/dev/QUICKSTART.md#training-megamolbart))
5. Stop training once validation_molecular_accuracy converges